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Building a recommender system to optimize donor engagement

  • Fort York Library 190 Fort York Boulevard Toronto, ON, M5V 0E6 Canada (map)

Overview

This is a project based workshop where you will work in teams to develop a simple algorithm to generate personalized recommendations for charitable donors, that are likely to convert into contributions.

Description:

This workshop is part of an ongoing series of project-based data science workshops, to prepare you for a career as a data scientist. We will focus getting hands-on experience and learning about fundamental data science techniques, all in the context of the Data Science Process. In this workshop, you will build a solid foundation in the theory behind recommendation systems, and learning how to build one from a real-world dataset. We will go over how to build, evaluate and fine-tune the model, as well as the necessary R and Python tech stack. After completing this workshop, you will understand how recommendation systems work, and have the know-how to build one yourself!

Project-based Data Science Education:

The format of these workshops are modelled after Harvard’s case-based method. We will follow the “flipped classroom” approach, where I will provide you with the material to prepare you for the workshop beforehand, but the sessions will primarily focus on group-work. You will work as a group to complete the project and I will supervise you and help you manage the details.

Portfolio Projects:

I will be creating this as a Kaggle In-Class competition so you’ll be able to automatically add this to your portfolio.

Building a recommender system to optimize donor engagement
100.00

This workshop is part of an ongoing series of project-based data science workshops, to prepare you for a career as a data scientist. We will focus getting hands-on experience and learning about fundamental data science techniques, all in the context of the Data Science Process. In this workshop, you will build a solid foundation in the theory behind recommendation systems, and learning how to build one from a real-world dataset. We will go over how to build, evaluate and fine-tune the model, as well as the necessary R and Python tech stack. After completing this workshop, you will understand how recommendation systems work, and have the know-how to build one yourself!

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